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Article citations


Nori, F.A. and Houghten, S. (2012) A Multi-Objective Genetic Algorithm with Side Effect Machines for Motif Discovery. 2012 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, San Diego, 9-12 May 2012, 257-282.

has been cited by the following article:

  • TITLE: Gapped Motif Discovery with Multi-Objective Genetic Algorithm

    AUTHORS: U. Angela Makolo, Salihu O. Suberu

    KEYWORDS: Genetic Algorithm, Motif Discovery, Multi-Objective Optimization

    JOURNAL NAME: Open Access Library Journal, Vol.3 No.3, March 30, 2016

    ABSTRACT: Motif discovery is one of the fundamental problems that have important applications in identifying drug targets and regulatory sites. Regulatory sites on DNA sequence normally correspond to shared conservative sequence patterns among the regulatory regions of correlated genes. These conserved sequence patterns are called motifs. Identifying motifs and corresponding instances is very important, so biologists can investigate the interactions between DNA and proteins, gene regulation, cell development and cell reaction under physiological and pathological conditions. In this work, we developed a motif finding algorithm based on a multi-objective genetic algorithm technique and incorporated the hypergeometric scoring function to enable it discover gapped motifs from organisms with challenging genomic structure such as the malaria parasite. The runtime performance of our resulting algorithm, EMOGAMOD (Extended Multi Objective Genetic Algorithm MOtif Discovery) was evaluated with that of some common motif discovery algorithms and the result was remarkable.